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Two Competing Models of How People Learn in Games

Reinforcement learning and stochastic fictitious play are apparent rivals as models of humans learning. They embody quite different assumptions about the processing of information and optimisation. This paper compares their properties and finds that they are far more similar than were thought. In particular, the expected motion of stochastic fictitious play and reinforcement learning with experimentation can both be written as a perturbed form of the evolutionary replicator dynamics. Therefore they will in many cases have the same asymptotic behaviour. In particular, they have identical local stability properties at mixed equilibria. The main identifiable difference between two models is speed: stochastic fictitious play gives rise to faster learning.

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Paper provided by Edinburgh School of Economics, University of Edinburgh in its series ESE Discussion Papers with number 51.

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Length: 38
Date of creation: Dec 2000
Date of revision:
Handle: RePEc:edn:esedps:51
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  1. Borgers, Tilman & Sarin, Rajiv, 1997. "Learning Through Reinforcement and Replicator Dynamics," Journal of Economic Theory, Elsevier, vol. 77(1), pages 1-14, November.
  2. Gale, John & Binmore, Kenneth G. & Samuelson, Larry, 1995. "Learning to be imperfect: The ultimatum game," Games and Economic Behavior, Elsevier, vol. 8(1), pages 56-90.
  3. Cheung, Yin-Wong & Friedman, Daniel, 1997. "Individual Learning in Normal Form Games: Some Laboratory Results," Games and Economic Behavior, Elsevier, vol. 19(1), pages 46-76, April.
  4. Gaunersdorfer Andrea & Hofbauer Josef, 1995. "Fictitious Play, Shapley Polygons, and the Replicator Equation," Games and Economic Behavior, Elsevier, vol. 11(2), pages 279-303, November.
  5. Levine, David & Dekel, Eddie & Fudenberg, Drew, 1999. "Payoff Information and Self-Confirming Equilibrium," Scholarly Articles 3200614, Harvard University Department of Economics.
  6. Nick Feltovich & John Duffy, 1999. "Does observation of others affect learning in strategic environments? An experimental study," International Journal of Game Theory, Springer;Game Theory Society, vol. 28(1), pages 131-152.
  7. John Duffy & Ed Hopkins, 2001. "Learning, Information and Sorting in Market Entry Games: Theory and Evidence," ESE Discussion Papers 78, Edinburgh School of Economics, University of Edinburgh.
  8. Andreas Blume & Douglas V. DeJong & George R. Neumann & N. E. Savin, 2002. "Learning and communication in sender-receiver games: an econometric investigation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(3), pages 225-247.
  9. Glenn Ellison & Drew Fudenberg, 1998. "Learning Purified Mixed Equilibria," Harvard Institute of Economic Research Working Papers 1817, Harvard - Institute of Economic Research.
  10. Sarin, Rajiv & Vahid, Farshid, 2001. "Predicting How People Play Games: A Simple Dynamic Model of Choice," Games and Economic Behavior, Elsevier, vol. 34(1), pages 104-122, January.
  11. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
  12. Benaim, Michel & Hirsch, Morris W., 1999. "Mixed Equilibria and Dynamical Systems Arising from Fictitious Play in Perturbed Games," Games and Economic Behavior, Elsevier, vol. 29(1-2), pages 36-72, October.
  13. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
  14. Rustichini, Aldo, 1999. "Optimal Properties of Stimulus--Response Learning Models," Games and Economic Behavior, Elsevier, vol. 29(1-2), pages 244-273, October.
  15. Drew Fudenberg & David K. Levine, 1998. "Learning in Games," Levine's Working Paper Archive 2222, David K. Levine.
  16. Vriend, Nicolaas J., 1997. "Will reasoning improve learning?," Economics Letters, Elsevier, vol. 55(1), pages 9-18, August.
  17. McKelvey Richard D. & Palfrey Thomas R., 1995. "Quantal Response Equilibria for Normal Form Games," Games and Economic Behavior, Elsevier, vol. 10(1), pages 6-38, July.
  18. David J. Cooper & Susan Garvin & John H. Kagel, 1997. "Signalling and Adaptive Learning in an Entry Limit Pricing Game," RAND Journal of Economics, The RAND Corporation, vol. 28(4), pages 662-683, Winter.
  19. Ken Binmore & Larry Samuelson, 1999. "Evolutionary Drift and Equilibrium Selection," Review of Economic Studies, Oxford University Press, vol. 66(2), pages 363-393.
  20. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
  21. Martin Posch, 1997. "Cycling in a stochastic learning algorithm for normal form games," Journal of Evolutionary Economics, Springer, vol. 7(2), pages 193-207.
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